Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 7/16/2026 has been entered.
The rejections related to 35 USC § 101 regarding to claims 1-22, 25 are withdrawn.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-2, 8, 10-11, 15-17, 22, 25 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 in view of Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/0392049 and Yuan et al. (Yuan) US 2022/0027784
In regard to claim 1, Qiu disclose A method for handling training of a machine learning model, the method performed by a coordinating entity that is operable to coordinate the training of the machine learning model at one or more network nodes and the method comprising: (Fig. 4, 7, and 14 [0029]-[0024][0059-[0062] [0122]- [0127] learning of a ML model and a task schedular to schedule to train the ML model at worker nodes)
in response to receiving a request to train the machine learning model: (Fig. 14, [0059]-[0063] [0122]-[0127] receive the worker node ask the task scheduler to work (train) the ML model)
selecting, from a plurality of network nodes, a first network node to train the machine learning model based on the information, (Fig. 11B, 14, [0059]-[0063] [0114]-[0116] [0120]-[0127], claim 4-5, selecting a specific number of worker node (worker 1, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task which represent the information.)
and transmission of the machine learning model towards the first network node for the first network node to train the machine learning model. ([0051]-[0059] [0122]-[0127] the worker pull the updated partial model parameter from the server)
But Qiu fail to explicitly disclose “initiating transmission of the machine learning model towards the first network node learning model.”
Johnson disclose initiating transmission of the machine learning model towards the first network node. ([0027][0055]-[0058] [0066]-[0067] transmit the initial ML model to the worker)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Johnson‘s ML model training into Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Johnson’s ML training with transmission of the ML to worker nodes would help to provide model delivery method into Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model delivery method by transmitting the model to the worker nodes would facilitate ML training.
But Qiu and Johnson fail to explicitly disclose “train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models; depending on a verification, the verification indicating the accuracy of the results of the machine learning model trained by the first network node.”
Jeuk disclose train the machine learning model based on information indicative of a performance of each of the plurality of network nodes, the information indicative of the performance of each of the plurality of network nodes comprising information indicative of a past performance of each of the plurality of network nodes, ([0049]-[0053][0066] [0066]-[0069] train the ML model based on the operational data such as current/recent performance for any of the individual nodes within the network topology)
the information indicative of the past performance of each of the plurality of network nodes comprising a measure of a past effectiveness of each of the plurality of network nodes in training machine learning models; ([0019][0045] [0049]-[0053][0066] [0066]-[0069] current/recent performance for any of the individual nodes within the network topology include metric of the performance, etc. such as number and type of system errors, ranking, network usage, overall system performance, etc.)
depending on a verification, depending on a verification, the verification indicating the accuracy of the results of the machine learning model trained by the first network node ([0077]-[0078] an accuracy threshold is evaluated to make the model sufficiently trained and this is the accuracy indication and based on the accuracy threshold condition, the model is sufficiently trained, here it discloses the action trigger condition. Note: please amend “the accuracy of the results” due to lack of antecedent basis.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Jeuk’s ML model training into Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Jeuk’s ML model training based on the past performance of nodes would help to provide more model training criteria into Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training criteria based on the past performance data would facilitate ML training.
But Qiu, Jeuk and Johnson fail to explicitly disclose “in response to receiving a training data from the first network node, adding the training data to a reference data, an amount of the training data added to the reference data,”
Yuan disclose in response to receiving a training data from the first network node, adding the training data to a reference data, ([0019]-[0022] [0036]-[0037] in response to receive the new data from the client, adding the new data to the old data)
an amount of the training data added to the reference data, ([0019]-[0022] [0029]-[0042] the new data is inputted to the model with the old data to do incremental training, the amount of the new data added is determined based on a condition, such as the accuracy threshold condition. Note: please further define the amount of the training data added to the reference data and based on what criteria, what is the reference data, etc. to help move forward the prosecution, call to discuss if necessary.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Yuan ‘s ML model training into Jeuk and Johnson, Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Yuan ‘s incremental ML model training would help to provide more ML model training method into Jeuk and Johnson, Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing incremental ML model training method would improve ML model training accuracy.
In regard to claim 2, Qiu and Johnson, Jeuk and Yuan disclose A method as claimed in claim 1,
Qiu disclose wherein: the machine learning model is: a previously untrained machine learning model; or a machine learning model previously trained by another network node of the plurality of network nodes. ([0097][0111] the model is trained at different workers)
In regard to claim 8, Qiu, Johnson, Jeuk and Yuan disclose A method as claimed in claim 1,
Qiu disclose the method comprising: in response to the first network node completing the training of the machine learning model, or in response to a failure of the first network node (10) to train the machine learning model: ([0105]-[-0109][0114]-[0116] the worker complete the training task)
selecting, from the plurality of network nodes, a second network node to further train the trained machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes, wherein the first network node and the second network node are different network nodes; (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task) and
initiating transmission of a request towards the second network node to trigger a transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model. ([0103]-[0116] communicate the model parameter with each of the other workers with the communication protocols in the worker group and for the other workers to train the model)
In regard to claim 10, Qiu, Johnson, Jeuk and Yuan disclose A method as claimed in claim 8,
Qiu disclose wherein: selecting the second network node is in response to receiving the trained machine learning model from the first network node. (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model when the model parameter are communicated with each of the other workers in the worker group and for the other workers to train the model)
In regard to claim 11, Qiu, Johnson, Jeuk and Yuan disclose A method as claimed in claim 8,
Qiu disclose wherein: the method is repeated in respect of at least one other different network node of the plurality of network nodes. (Fig. 11B, 14, [0039] [0054]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, the process is repeated, selecting another worker node (worker 2, for example) form the worker group to training the ML model when the model parameter are communicated with each of the other workers in the worker group and for the other workers to train the model)
In regard to claim 15, claim 15 disclose A coordinating entity claim (Fig. 4, 410, [0051]-[0055] [0061]-[0063] [116]-[0127] task scheduler and server ) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 16, claim 16 disclose A coordinating entity claim (Fig. 4, 410, [0051]-[0055] [0061]-[0063] [116]-[0127] task scheduler and server ) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
In regard to claim 17, Qiu disclose A method for handling training of machine learning model, the method performed by a system comprising a plurality of network nodes and a coordinating entity that is operable to coordinate training of the machine learning model at one or more of the plurality of network nodes, the method comprising: (Fig. 4, 7, and 14 [0029]-[0024][0059-[0062] [0122]- [0127] learning of a ML model and a task schedular to schedule to train the ML model at worker nodes) corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
the first network node, (Fig. 14, [0059]-[0063] [0122]-[0127] receive the worker node ask the task scheduler to work (train) the ML model)
in response to receiving the machine learning model from the coordinating entity, ([0051]-[0059] [0122]-[0127] the worker pull the updated partial model parameter from the server)
training the machine learning model using training data that is available to the first network node. (Fig. 11B, 14, [0030]-[0033][0050]-[0055] [0059]-[0063] [0114]-[0116] [0120]-[0127], training the ML model with the learning dataset)
In regard to claim 22, Qiu, Johnson, Jeuk and Yuan disclose A method as claimed in claim17,
Qiu wherein: the training data that is available to the first network node comprises data from one or more devices registered to the first network node. (Fig. 4, [0051]-[0055] the training data is from the learning dataset 416 database which can communicate with)
In regard to claim 25, claim 25 disclose a computer program product claim corresponding to the method claim 1 above and, therefore, is rejected for the same reasons set forth in the rejections of claim 1.
Claims 3, 9, 12-13, 18 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/0392049, and Yuan et al. (Yuan) US 2022/0027784 as applied to claim 1, further in view of Heinrich et al. (Heinrich) US 2020/0226461
In regard to claim 3, Qiu and Johnson, Jeuk and Yuan disclose A method as claimed in claim 1,
Qiu disclose the method comprising: in response to receiving the trained machine learning model from the first network node: (Fig. 4, [0051]-[0055] server receive the trained ML mode from the worker nodes)
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics.”
Heinrich disclose checking whether the trained machine learning model meets a predefined threshold for one or more performance metrics. ([0030]-[0037][0053]-[0055] measure performance metrics of the ML model require the performance metrics higher than the quantile threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into and Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 9, Qiu, Johnson, Jeuk, Yuan and Heinrich, disclose A method as claimed in claim 3,
Qiu disclose the method comprising: in response to the first network node completing the training of the machine learning model, or in response to a failure of the first network node (10) to train the machine learning model: ([0105]-[-0109][0114]-[0116] the worker complete the training task)
selecting, from the plurality of network nodes, a second network node to further train the trained machine learning model based on information indicative of a performance of each of the plurality of network nodes and/or information indicative of a quality of a network connection to each of the plurality of network nodes, wherein the first network node and the second network node are different network nodes; (Fig. 11B, 14, [0059]-[0063][0103]-[0116] [0120]-[0127], claim 4-5, selecting another worker node (worker 2, for example) form the worker group to training the ML model based on the worker nodes are idle or not performing a ML task)
initiating transmission of a request towards the second network node to trigger a transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model; ([0103]-[0116] communicate the model parameter with each of the other workers with the communication protocols in the worker group and for the other workers to train the model) and
if the trained machine learning model fails to meet the one or more performance metrics, selecting the second network node to further train the trained machine learning model and initiating the transmission of the request towards the second network node to trigger the transfer; or initiating transmission of the trained machine learning model towards an entity that initiated transmission of the request to train the machine learning model. ([0051]-[0059][0095][0096] [0122]-[0127] the worker pushed the trained model parameter to the server which send the transmission request to train the ML model for the worker nodes)
But Qiu and John, Jeuk and Yuan fail to explicitly disclose “if the trained machine learning model meets the one or more performance metrics,”
Heinrich disclose if the trained machine learning model meets the one or more performance metrics, ([0030]-[0037][0053]-[0055] measure performance metrics of the ML model require the performance metrics higher than the quantile threshold)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 12, Qiu, Johnson, Jeuk, Yuan disclose A method as claimed in claim1,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “wherein: the information indicative of the performance of each of the plurality of network nodes comprises: information indicative of an expected performance of each of the plurality of network nodes.”
Heinrich disclose wherein: the information indicative of the performance of each of the plurality of network nodes comprises information indicative of an expected performance of each of the plurality of network nodes. ([0030]-[0041] the information is indicative of a priori information of the expected performance of the nodes)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 13, Qiu, Johnson, Jeuk and Yuan and Heinrich disclose A method as claimed in claim 12,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “wherein: the information indicative of the expected performance of each of the plurality of network nodes comprises: a measure of an available compute capacity of each of the plurality of network nodes; and/or a measure of the quality and/or an amount of training data available to each of the plurality of network nodes.”
Heinrich disclose wherein: the information indicative of the expected performance of each of the plurality of network nodes comprises: a measure of an available compute capacity of each of the plurality of network nodes; and/or a measure of the quality and/or an amount of training data available to each of the plurality of network nodes. ([0020] [0030]-[0041] the information is indicative of a priori information of the expected performance of the node, include available computational resources, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
In regard to claim 18, Qiu and Johnson, Jeuk and Yuan disclose A method as claimed in claim 17,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “the method performed by the first network node comprising: continuing to train the machine learning model using the training data that is available to the first network node until a maximum accuracy for the trained machine learning model is reached and/or until the first network node runs out of computational capacity to train the machine learning model.”
Heinrich disclose the method performed by the first network node comprising: continuing to train the machine learning model using the training data that is available to the first network node until a maximum accuracy for the trained machine learning model is reached and/or until the first network node runs out of computational capacity to train the machine learning model. ([0020] [0028]-[0041] [0054] train the ML using the training data until the available computational resources is fully used.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Heinrich‘s ML training performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying ML training performance metrics would help to provide model training evaluation method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 and Yuan et al. (Yuan) US 2022/0027784 as applied to claim 1, further in view of Hagdahl et al. (Hagdahl) US 11030134
In regard to claim 14, Qiu, Johnso, Jeuk and Yuan disclose A method as claimed in claim1,
But Qiu, Johnson and Jeuk and Yuan fail to explicitly disclose “wherein: the information indicative of the quality of the network connection to each of the plurality of network node comprises: a measure of an available throughput of the network connection to each of the plurality of network nodes; a measure of a latency of the network connection to each of the plurality of network nodes; and/or a measure of a reliability of the network connection to each of the plurality of network.”
Hagdahl disclose wherein: the information indicative of the quality of the network connection to each of the plurality of network node comprises: a measure of an available throughput of the network connection to each of the plurality of network nodes; a measure of a latency of the network connection to each of the plurality of network nodes; and/or a measure of a reliability of the network connection to each of the plurality of network. (col. 8, line 22-col. 9, line 21, the performance metric is QOS of the connection with latency, throughput, etc.)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Hagdahl‘s performance metrics into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Heinrich‘s identifying performance metrics would help to provide model training evaluation method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing model training evaluation method by performance metrics would facilitate ML training.
Claims 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 and Yuan et al. (Yuan) US 2022/0027784 as applied to claim 1, further in view of Healy et al. (Healy) US 20210406369
In regard to claim 19, Qiu, Johnson, Jeuk, Yuan disclose A method as claimed in claim 17,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “the method performed by the first network node comprising: in response to receiving a request for the training data, wherein transmission of the request is initiated by the coordinating entity, initiating transmission of the training data towards the coordinating entity.”
Healy disclose the method performed by the first network node comprising: in response to receiving a request for the training data, wherein transmission of the request is initiated by the coordinating entity, initiating transmission of the training data towards the coordinating entity. ([0025]-[0033] transmit the training data from the device to the server in response to a request through a network interface at the server)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Healy‘s AI models into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Healy‘s method of requesting training data would help to provide training data gathering method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training data gathering method would facilitate ML training.
In regard to claim 20, Qiu, Johnson, Jeuk, Yuan disclose A method as claimed in claim17,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “the method performed by the first network node comprising: initiating transmission of the trained machine learning model towards the coordinating entity.”
Healy disclose the method performed by the first network node comprising: initiating transmission of the trained machine learning model towards the coordinating entity. ([0025]-[0033] transmit the training data from the device to the server in response to a request through a network interface at the server)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Healy‘s AI models into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Healy‘s method of requesting training data would help to provide training data gathering method into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training data gathering method would facilitate ML training.
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Qiu et al. (Qiu) US 20210133555 and Johnson et al. (Johnson) US 2021/0089887, Jeuk et al. (Jeuk) US 2021/039204 and Yuan et al. (Yuan) US 2022/0027784 as applied to claim 1, further in view of Kim et al. (KIm) US 20210374503
In regard to claim 21, Qiu, Johnson, Jeuk and Yuan disclose A method as claimed in claim17,
But Qiu and Johnson, Jeuk and Yuan fail to explicitly disclose “the method performed by the first network node comprising: in response to receiving a request to trigger a transfer of the trained machine learning model from the first network node to the second network node: initiating the transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model.
Kim disclose the method performed by the first network node comprising: in response to receiving a request to trigger a transfer of the trained machine learning model from the first network node to the second network node: initiating the transfer of the trained machine learning model from the first network node to the second network node for the second network node to further train the machine learning model. ([0030][0043]-[0048] in response to a request to transfer the model, transfer the model from the first node to the second node to make the second node to train the model with TCP/IP communication protocol which include the request and response communications)
It would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made to incorporate Kim’s distributed training of neural networks into Yuan, Jeuk, Johnson and Qiu’s invention as they are related to the same field endeavor of model training and learning. The motivation to combine these arts, as proposed above, at least because Kim’s distributed training of neural networks would help to provide training models between the nodes into Yuan, Jeuk, Johnson and Qiu’s system. Therefore it would have been obvious to one having ordinary skill in the art before the effective filing data of the claimed invention was made that providing training models between the nodes would facilitate ML training.
Allowable Subject Matter
Claims 4-7 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Response to Arguments
Applicant’s arguments with respect to claims 1-22, 24-25 filed on 7/16/2026 have been considered but are moot because the arguments do not apply to the current rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure.
U.S. Patent Documents PATENT DATE INVENTOR(S) TITLE
US 11671341 B2 2023-06-06 Jain et al.
Network Monitoring Method And Network Monitoring Apparatus
Jain et al. disclose A network monitoring method includes: monitoring, at a network node, one or more network statistics for each of one or more objects exchanged between a client end point and a server end point through a network connection, the network node being located between the client end point and the server end point; and determining one or more QoS metrics for each of the one or more objects based on the network statistics for respective one of the one or more objects... see abstract.
US 20210201190 A1 2021-07-01 Edgar
MACHINE LEARNING MODEL DEVELOPMENT AND OPTIMIZATION PROCESS THAT ENSURES PERFORMANCE VALIDATION AND DATA SUFFICIENCY FOR REGULATORY APPROVAL
Edgar disclose Machine learning model development and optimization tools are provided that ensure performance validation and data sufficiency for regulatory approval. According to an embodiment, a computer implemented method can comprise training a machine learning model to perform an inferencing task on an initial set of data samples included in a sample population. In various embodiments, the model can include a medical AI model. The method further comprises determining, by the system, subgroup performance measures for subgroups of the data samples respectively associated with different metadata factors, wherein the subgroup performance measures reflect performance accuracy of the machine learning model with respect to the subgroups. The method further comprises determining, by the system, whether the machine learning model meets an acceptable level of performance for deployment in a field environment based on whether the subgroup performance measures respectively satisfy a threshold subgroup performance measure…. See abstract.
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XUYANG XIA
Primary Examiner
Art Unit 2143
/XUYANG XIA/Primary Examiner, Art Unit 2143
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